php-ml/tests/DimensionReduction/PCATest.php
2018-10-28 07:44:52 +01:00

102 lines
3.3 KiB
PHP

<?php
declare(strict_types=1);
namespace Phpml\Tests\DimensionReduction;
use Phpml\DimensionReduction\PCA;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Exception\InvalidOperationException;
use PHPUnit\Framework\TestCase;
class PCATest extends TestCase
{
public function testPCA(): void
{
// Acceptable error
$epsilon = 0.001;
// First a simple example whose result is known and given in
// http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
$data = [
[2.5, 2.4],
[0.5, 0.7],
[2.2, 2.9],
[1.9, 2.2],
[3.1, 3.0],
[2.3, 2.7],
[2.0, 1.6],
[1.0, 1.1],
[1.5, 1.6],
[1.1, 0.9],
];
$transformed = [
[-0.827970186], [1.77758033], [-0.992197494],
[-0.274210416], [-1.67580142], [-0.912949103], [0.0991094375],
[1.14457216], [0.438046137], [1.22382056], ];
$pca = new PCA(0.90);
$reducedData = $pca->fit($data);
// Due to the fact that the sign of values can be flipped
// during the calculation of eigenValues, we have to compare
// absolute value of the values
array_map(function ($val1, $val2) use ($epsilon): void {
self::assertEquals(abs($val1), abs($val2), '', $epsilon);
}, $transformed, $reducedData);
// Test fitted PCA object to transform an arbitrary sample of the
// same dimensionality with the original dataset
foreach ($data as $i => $row) {
$newRow = [[$transformed[$i]]];
$newRow2 = $pca->transform($row);
array_map(function ($val1, $val2) use ($epsilon): void {
self::assertEquals(abs($val1), abs($val2), '', $epsilon);
}, $newRow, $newRow2);
}
}
public function testPCAThrowWhenTotalVarianceOutOfRange(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Total variance can be a value between 0.1 and 0.99');
new PCA(0., null);
}
public function testPCAThrowWhenNumFeaturesOutOfRange(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Number of features to be preserved should be greater than 0');
new PCA(null, 0);
}
public function testPCAThrowWhenParameterNotSpecified(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Either totalVariance or numFeatures should be specified in order to run the algorithm');
new PCA();
}
public function testPCAThrowWhenBothParameterSpecified(): void
{
$this->expectException(InvalidArgumentException::class);
$this->expectExceptionMessage('Either totalVariance or numFeatures should be specified in order to run the algorithm');
new PCA(0.9, 1);
}
public function testTransformThrowWhenNotFitted(): void
{
$samples = [
[1, 0],
[1, 1],
];
$pca = new PCA(0.9);
$this->expectException(InvalidOperationException::class);
$this->expectExceptionMessage('PCA has not been fitted with respect to original dataset, please run PCA::fit() first');
$pca->transform($samples);
}
}